Central venous pressure estimation with force-coupled ultrasound of the internal jugular vein

We estimate central venous pressure (CVP) with force-coupled ultrasound imaging of the internal jugular vein (IJV). We acquire ultrasound images while measuring force applied over the IJV by the ultrasound probe imaging surface. We record collapse force, the force required to completely occlude the vein, in 27 healthy subjects. We find supine collapse force and jugular venous pulsation height (JVP), the clinical noninvasive standard, have a linear correlation coefficient of r2 = 0.89 and an average absolute difference of 0.23 mmHg when estimating CVP. We perturb our estimate negatively by tilting 16 degrees above supine and observe decreases in collapse force for every subject which are predictable from our CVP estimates. We perturb venous pressure positively to values experienced in decompensated heart failure by having subjects perform the Valsalva maneuver while the IJV is being collapsed and observe an increase in collapse force for every subject. Finally, we derive a CVP waveform with an inverse three-dimensional finite element optimization that uses supine collapse force and segmented force-coupled ultrasound data at approximately constant force.

The load cell measures uniaxial force along the z-axis as shown in Supplementary Figure 1B. The load cell was selected for its balance of compact size and sufficient overload protection. While the manufacturer specifies a safe axial accidental overload of 1000% of rated output (RO), or 250 lbf (1100N), the probe assembly will be subjected to off-axis forces and moments during use, due to the offset load path and user handling. In this case it is recommended that the maximum combined loading should not routinely exceed the stress value calculated by the manufacturer's equation below.
When fully assembled with the ultrasound probe, the force-coupling was calibrated with a combination of calibration weights while fixed in its vertical orientation with a conventional scale with a capacity of 50 lbs and a sensitivity of 0.1 lbs (Accuteck Packaging, Foxboro, Massachusetts, United States). We see in Supplementary Figure

Orthogonal IJV Compression
When acquiring force-coupled ultrasound images of the short-axis cross-section of the IJV under compression, it is important that the angle of incidence of the probe with the long-axis plane of the IJV is orthogonal. Achieving this will minimize the necessary force to occlude the IJV in comparison to nonorthogonal angles. This angle of incidence can be tracked in the LabVIEW front panel on the system tablet which is shown in Supplementary Figure 2A. When viewing ultrasound images in 3-D mode in the EPIQ 7C system using the XL14-3 probe, one can confirm orthogonality with the long axis plane in two different ways. The first way is by looking at the 3-D rendering which shows white walls in the IJV when walls not at the front of the structure are visible. Seeing these white-rendered walls around the anterior and posterior walls of the IJV means that orthogonality with the long-axis plane is not achieved. The second way is by looking at just the long-axis cross-section. Seeing a completely horizontal long-axis cross-section implies orthogonality with not only the long-axis plane, but also the long-axis itself.
Supplementary Figure 2B shows an orthogonal incidence while Supplementary Figure

Faster R-CNN for Automated IJV Detection
To automate the detection of the IJV in a raw ultrasound image, we train the object detection convolutional neural network Faster R-CNN with segmented ultrasound images of the short-axis crosssection of the IJV. After the detector is trained, it is used to detect the IJV in synchronized force-coupled ultrasound images. A high confidence threshold is used to make incorrect detections exceedingly unlikely. Those which pass the primary search select the correct structure as the IJV in the first frame examined. Those which pass the secondary search select the correct structure in one of the ten frames following the first frame after failing to detect the IJV in the first frame. In the case that neither the primary or secondary search pass, the IJV is detected manually by the user clicking inside the IJV in a displayed ultrasound image to initiate segmentation.

Collapse Force Detection and Visual JVP Estimation
As mentioned in the main text, the collapse force is automatically sensed during segmentation as the first frame to reach the collapse force area threshold of 0.5 mm 2 . In Supplementary Figure 4A, we track both IJV area and external force as functions of time to add explanation for where the collapse force is sensed before any uncertainty in cardiac cycle or segmentation is quantified. The collapse force in our force-coupled ultrasound method is analogous to quantifying JVP as both seek to estimate venous pressure.
The JVP is most conventionally estimated by measuring the height above the sternal angle. In this study, we aimed to make the method more quantitative by looking for pulsations just above the clavicle while reclining the subject and noting the angle at which pulsations first start to be visible. The precision of this angle is to the single degree, which corresponds to a JVP precision of about 0.1 mmHg after converting from cmH20 to mmHg. For every subject, 10 cm is assumed to be the distance from the center of the right atrium to the pulsation viewing window just above the clavicle. The JVP is calculated from the following equation: = 0.7356 * 10 sin (S.3) Where the four-digit decimal is the conversion from cmH20 to mmHg, 10 is the assumed distance in cm from the center of the right atrium to the base of the neck, and theta is the largest angle at which IJV pulsations can be seen at the base of the neck.

Deming Regressions of Perturbed Collapse Force and JVP
In the main text, a linear least squares line of best fit, which only accounts for uncertainty in the dependent variable, is generated relating supine normal breathing collapse force and JVP measurement because the uncertainty in the collapse force is far greater than the repeatability uncertainty in JVP.
However, in the negative and positive perturbations, the uncertainty in the JVP measurement is more equal in magnitude to the uncertainty in collapse force. When elevating the subject to 16 degrees, the JVP measurement is adjusted assuming the distance from the right atrium to the base of the neck is 10 cm to account for the hydrostatic pressure decrease without allowing venous pressures below 0 mmHg.
Yet the population of subjects ranges in height, which adds uncertainty to the measurement. Regarding the Valsalva maneuver, a digital manometer measures airway pressure, which is used as a proxy for venous pressure. The uncertainty here stems from the imprecision of the manometer measurement and the indirectness of the airway pressure proxy, yielding an uncertainty of 3 mmHg on each side.
Furthermore, the cardiac cycle variation of collapse force decreases because the right atrium is unable to expand and contract during Valsalva.
A Deming regression line accounts for uncertainty in both the dependent and independent variable and is used to produce lines of best fit for the supine normal breathing collapse force and JVP, the 16-degree elevation collapse force and hydrostatic offset adjusted JVP, and the Valsalva collapse force and airway pressure in Supplementary Figure 5A, 5B, and 5C respectively. The r 2 correlation coefficient works to provide a certain level of confidence in the Deming regression lines in that low r 2 should yield low confidence and high r 2 should yield higher confidence [3]. That said, the progressively steeper slopes of the Deming regression lines at higher venous pressures, shown in Supplementary Figure 5D, lends credence to the theory that as venous pressure increases, a smaller percentage of the external force applied is dedicated to collapsing the IJV. Evidence against this theory is also present given the large gap in assumed venous pressure between the supine and Valsalva Deming regression lines.

"Average" Filtering for Noisy Carotid Artery Area Waveform
The carotid artery is fully in the far field of the ultrasound image, which decreases image resolution, yielding a noisy carotid area waveform. An in-band filter is developed in an effort to decrease in-band noise to produce a carotid area waveform of a more typical and consistent morphology in Figure   4E.  Figure 6D shows an overlay of the raw carotid area waveform and the "average" filtered carotid area waveform. In Figure   4E, the "average" filtered waveform goes through an additional 3-point moving average filter.
Supplementary Figure 6: Process of "average filter". "Samples" signifies the number of ultrasound frames from which the segmentation was originally derived and is the x-axis for each of the graphs. (A) Raw carotid area waveform with beats numbered. (B) Average beat, normalized in "y" and interpolated in "x". "y" is unitless and normalized. (C) Standard deviation at each normalized and interpolated beat sample. "y" is unitless and normalized. (D) Overlay of raw carotid area and carotid area after going through "average" filter.

Three-dimensional Finite Element Modeling for Orthogonal Plane Imaging
One way of expanding the information input to our three-dimensional forward finite element model when attempting to solve the inverse problem of venous pressure waveform estimation would be utilizing the three-dimensional imaging capabilities. Supplementary Figure 6 illustrates how we would be able to inform pressure with input from two orthogonal planes captured by the XL14-3 xMATRIX probe.
This would allow us to compare IJV compression in three dimensions instead of two. In theory, this additional relevant information should lead to a waveform estimate of at least the same accuracy if not higher accuracy than our current two-dimensional observations. The drawbacks are minimal but there are slight losses in frame rate and resolution when viewing ultrasound images in this way. Further exploration of our three-dimensional inverse optimization with the additional long-axis compression observations is an apt next step in improving our venous pressure waveform methodology in addition to the inclusion of the carotid artery pulsation in our forward model.